ABSTRACT
With rapidly growing amount of data available on the web, it becomes increasingly likely to obtain data from different perspectives for multi-view learning. Some successive examples of web applications include recommendation and target advertising. Specifically, to predict whether a user will click an ad in a query context, there are available features extracted from user profile, ad information and query description, and each of them can only capture part of the task signals from a particular aspect/view. Different views provide complementary information to learn a practical model for these applications. Therefore, an effective integration of the multi-view information is critical to facilitate the learning performance.
In this paper, we propose a general predictor, named multi-view machines (MVMs), that can effectively explore the full-order interactions between features from multiple views. A joint factorization is applied for the interaction parameters which makes parameter estimation more accurate under sparsity and renders the model with the capacity to avoid overfitting. Moreover, MVMs can work in conjunction with different loss functions for a variety of machine learning tasks. The advantages of MVMs are illustrated through comparison with other methods for multi-view prediction, including support vector machines (SVMs), support tensor machines (STMs) and factorization machines (FMs).
A stochastic gradient descent method and a distributed implementation on Spark are presented to learn the MVM model. Through empirical studies on two real-world web application datasets, we demonstrate the effectiveness of MVMs on modeling feature interactions in multi-view data. A 3.51\% accuracy improvement is shown on MVMs over FMs for the problem of movie rating prediction, and 0.57\% for ad click prediction.
- Yuanzhe Cai, Miao Zhang, Dijun Luo, Chris Ding, and Sharma Chakravarthy. Low-order tensor decompositions for social tagging recommendation. In WSDM, pages 695--704. ACM, 2011. Google ScholarDigital Library
- Bokai Cao, Lifang He, Xiangnan Kong, Philip S. Yu, Zhifeng Hao, and Ann B. Ragin. Tensor-based multi-view feature selection with applications to brain diseases. In ICDM, pages 40--49. IEEE, 2014. Google ScholarDigital Library
- John Duchi, Elad Hazan, and Yoram Singer. Adaptive subgradient methods for online learning and stochastic optimization. The Journal of Machine Learning Research, 12:2121--2159, 2011. Google ScholarDigital Library
- Joseph E Gonzalez, Reynold S Xin, Ankur Dave, Daniel Crankshaw, Michael J Franklin, and Ion Stoica. GraphX: Graph processing in a distributed dataflow framework. In OSDI, pages 599--613. USENIX, 2014. Google ScholarDigital Library
- Liangjie Hong, Aziz S Doumith, and Brian D Davison. Co-factorization machines: modeling user interests and predicting individual decisions in twitter. In WSDM, pages 557--566. ACM, 2013. Google ScholarDigital Library
- Yu-Chin Juan, Yong Zhuang, and Wei-Sheng Chin. LIBFFM: A Library for Field-aware Factorization Machines, 2015. Software available at http://www.csie.ntu.edu.tw/cjlin/libffm.Google Scholar
- Tamara G Kolda and Brett W Bader. Tensor decompositions and applications. SIAM review, 51(3):455--500, 2009. Google ScholarDigital Library
- Yehuda Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In KDD, pages 426--434. ACM, 2008. Google ScholarDigital Library
- Gert RG Lanckriet, Nello Cristianini, Peter Bartlett, Laurent El Ghaoui, and Michael I Jordan. Learning the kernel matrix with semidefinite programming. The Journal of Machine Learning Research, 5:27--72, 2004. Google ScholarDigital Library
- Steffen Rendle. Factorization machines. In ICDM, pages 995--1000. IEEE, 2010. Google ScholarDigital Library
- Steffen Rendle. Factorization machines with libFM. Intelligent Systems and Technology, 3(3):57, 2012. Google ScholarDigital Library
- Steffen Rendle and Lars Schmidt-Thieme. Pairwise interaction tensor factorization for personalized tag recommendation. In WSDM, pages 81--90. ACM, 2010. Google ScholarDigital Library
- Dacheng Tao, Xuelong Li, Weiming Hu, Stephen Maybank, and Xindong Wu. Supervised tensor learning. In ICDM, pages 8--pp. IEEE, 2005. Google ScholarDigital Library
- Vladimir Vapnik. The nature of statistical learning theory. Springer Science & Business Media, 2000. Google ScholarCross Ref
- Cong Xie, Ling Yan, Wu-Jun Li, and Zhihua Zhang. Distributed power-law graph computing: Theoretical and empirical analysis. In NIPS, pages 1673--1681, 2014.Google Scholar
- Ling Yan, Wu-jun Li, Gui-Rong Xue, and Dingyi Han. Coupled group lasso for web-scale CTR prediction in display advertising. In ICML, pages 802--810, 2014.Google ScholarDigital Library
- Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauley, Michael J Franklin, Scott Shenker, and Ion Stoica. Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. In NSDI, pages 2--2. USENIX, 2012. Google ScholarDigital Library
Index Terms
- Multi-view Machines
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